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大规模电动汽车集群分层实时优化调度
引用本文:潘振宁,张孝顺,余涛,王德志.大规模电动汽车集群分层实时优化调度[J].电力系统自动化,2017,41(16):96-104.
作者姓名:潘振宁  张孝顺  余涛  王德志
作者单位:华南理工大学电力学院, 广东省广州市 510640,华南理工大学电力学院, 广东省广州市 510640,华南理工大学电力学院, 广东省广州市 510640,华南理工大学电力学院, 广东省广州市 510640
基金项目:国家重点基础研究发展计划(973计划)资助项目(2013CB228205);国家自然科学基金资助项目(51477055)
摘    要:为解决电动汽车的大规模实时优化调度问题,根据接入电动汽车不同的期望充电完成时间,将其划分为若干个不同优先级的电动汽车集群,在满足车主充电需求、配电网安全运行的同时,建立了考虑电动汽车充放电的大规模集群实时优化调度模型。该调度模型主要分为两个层次:首先,采用灰狼优化(GWO)算法对上层调度进行求解,从而获得各个电动汽车集群的充放电策略;然后,利用提出的能量缓冲一致性算法,制定出集群内的各辆电动汽车的底层充放电策略。仿真算例表明:所搭建的集群优化模型能明显降低电动汽车的大规模实时优化调度难度,同时,GWO算法和能量缓冲一致性算法在求解电动汽车的大规模优化调度问题上,更具有实用性和快速性。

关 键 词:能量缓冲一致性  大规模电动汽车  集群分层优化  充放电优化  灰狼优化算法
收稿时间:2016/9/19 0:00:00
修稿时间:2017/5/8 0:00:00

Hierarchical Real-time Optimized Dispatching for Large-scale Clusters of Electric Vehicles
PAN Zhenning,ZHANG Xiaoshun,YU Tao and WANG Dezhi.Hierarchical Real-time Optimized Dispatching for Large-scale Clusters of Electric Vehicles[J].Automation of Electric Power Systems,2017,41(16):96-104.
Authors:PAN Zhenning  ZHANG Xiaoshun  YU Tao and WANG Dezhi
Affiliation:School of Electric Power, South China University of Technology, Guangzhou 510640, China,School of Electric Power, South China University of Technology, Guangzhou 510640, China,School of Electric Power, South China University of Technology, Guangzhou 510640, China and School of Electric Power, South China University of Technology, Guangzhou 510640, China
Abstract:This paper presents a real-time optimized dispatching model for large-scale cluster electric vehicles(EVs)to achieve the charging demand and safe operation of distribution network. For each new optimization scenario, the accessed EVs can cluster according to their desired completion time. The entire dispatching process for charging/discharging strategy can be divided into two steps, i. e. , the upper dispatching based on grey wolf optimization(GWO)algorithm for each cluster, and the bottom layer based on energy buffer consensus for each EV in the corresponding cluster. Simulation results demonstrate that the proposed model can significantly facilitate the real-time large-scale optimal dispatching of EVs, while GWO algorithm and energy buffer consensus are suitable to solve large-scale optimal dispatching with superior performance on availability and convergence rate. This work is supported by National Basic Research Program of China(973 Program)(No. 2013CB228205)and National Natural Science Foundation of China(No. 51477055).
Keywords:energy buffer consensus  large-scale electric vehicles  hierarchical optimization of clusters  charging/discharging optimization  grey wolf optimization algorithm
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